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pro vyhledávání: '"Ramos, Jerome"'
Recently, researchers have investigated the capabilities of Large Language Models (LLMs) for generative recommender systems. Existing LLM-based recommender models are trained by adding user and item IDs to a discrete prompt template. However, the dis
Externí odkaz:
http://arxiv.org/abs/2407.05033
Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to re
Externí odkaz:
http://arxiv.org/abs/2402.05810
In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to construct desired structures in a simulated environment such as Minecraft
Externí odkaz:
http://arxiv.org/abs/2305.05754
Autor:
Cooper, Matt, Lee, Jun Ki, Beck, Jacob, Fishman, Joshua D., Gillett, Michael, Papakipos, Zoë, Zhang, Aaron, Ramos, Jerome, Shah, Aansh, Littman, Michael L.
Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory's well-known "folk theorem." There is a cost, however, to a player for generating these disincentives. In this work, we seek to m
Externí odkaz:
http://arxiv.org/abs/1908.08641
Autor:
Nisperos, Bryelle Timothy C., Pascual, Joshua Yñigo N., Ramos, Jerome Patrick S., Intal, Grace Lorraine D.
Publikováno v:
Proceedings of the International Conference on Industrial Engineering & Operations Management; 11/3/2021, p1708-1718, 11p